Table of Contents

The first few posts to this blog will be most coherent if they are read in chronological order. A new entry will be posted each Wednesday morning. To see the latest posts in blog format, click here. To get updates when new posts appear, you can subscribe by RSS, or follow me on google+ or twitter @jejomath.

I – Introduction

1. Different names for data analysis
2. What is data?
3. Filling in the gaps – Probability Distributions
4. Configuration Spaces and the Meaning of Probability
   
5. Continuous Bayes’ Theorem

II – Basic Modeling and Supervised Learning

1. The geometry of linear regression
2. General regression and overfitting
3. Optimization
4. The Curse of Dimensionality
5. Principle Component Analysis
6. Visualization and Projection
7. Nearest Neighbors Classification
8. Data Normalization
9. K Nearest Neighbors Classification
10. Support Vector Machines
11. Logistic Regression
12. Kernels
13. Multi-class classification
14. Genetic Algorithms and Symbolic Regression

III – Case Studies

1. Case Study 1: The Iris Data Set
2. Case Study 2: Tokens in Census Data
3. Case Study 3: Free Form Text
   
4. Case Study 4: Resonance and Robots
5. Case Study 5: Wavelets
   
6. Case Study 6: Digital Images
   

IV – Multi-level Modeling and Supervised Learning

1. Neural Networks 1: The Neuron
2. Neural Networks 2: Evaluation
3. Neural Networks 3: Training
4. Decision Trees
5. Random Forests
6. Mixture Models
7. Gaussian Kernels
8. Convolutional Neural Networks
9. Recurrent Neural Networks
10. Neural Networks, linear transformations and Word Embeddings
11. The TensorFlow perspective on Neural Networks
12. Rolling and Unrolling RNNs
13. LSTMs

V – Unsupervised Learning

1. K-means
2. Intrinsic vs. Extrinsic structure
3. Graphs and Networks
4. Clusters and DBSCAN
5. Mapper and the choice of scale
6. Spectral Clustering
7. Modularity – Measuring Cluster Separation
8. TILO/PRC Clustering
9. Duality and Co-clustering
10. K-modes

VI – Big data

1. Distributed learning
2. PageRank
3. MapReduce
4. GPUs and Neural Networks

VII – Misc.

1. Statistics Vs. Heuristics
2. Precision, Recall and AUCs
3. P-Values